Deep learning in stock portfolio selection and predictions

Alzaman, Chaher

doi:10.1016/j.eswa.2023.121404

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Deep learning methods can rapidly process extensive and intricate datasets, representing nonlinear functions without being constrained by dimensionality. This characteristic helps investors extract valuable insights and make well-informed decisions in complex market scenarios [ 16 ]. Our study is at the forefront of applying deep learning techniques to stock return prediction and factor investment.…”

Section: Introductionmentioning

confidence: 99%

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks

Wang,

Chen

2024

PLoS ONE

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Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

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Section: Introductionmentioning

confidence: 99%